TY - GEN

T1 - Real-time human activity recognition from wireless sensors using evolving fuzzy systems.

AU - Andreu, Javier

AU - Angelov, Plamen

N1 - "©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE." "This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder."

PY - 2010/7

Y1 - 2010/7

N2 - A new approach to real-time knowledge extraction from streaming data generated by wearable wireless accelerometers based on self-learning evolving fuzzy rule-based classifier is proposed and evaluated in this paper. Based on experiments with real subjects we collected data from 18 different classifieds activities. After preprocessing and classifying data depending on the sequence of activities regarding time, we achieved up to 99.81% of accuracy in recognizing a sequence of activities. This technique allows re-training the system as long as the application is running on the wearable intelligent/smart sensor, getting a better classification rate throughout the time without an increase of the delay in performance. (c) IEEE Press

AB - A new approach to real-time knowledge extraction from streaming data generated by wearable wireless accelerometers based on self-learning evolving fuzzy rule-based classifier is proposed and evaluated in this paper. Based on experiments with real subjects we collected data from 18 different classifieds activities. After preprocessing and classifying data depending on the sequence of activities regarding time, we achieved up to 99.81% of accuracy in recognizing a sequence of activities. This technique allows re-training the system as long as the application is running on the wearable intelligent/smart sensor, getting a better classification rate throughout the time without an increase of the delay in performance. (c) IEEE Press

KW - activity recognition

KW - evolving fuzzy classifier

U2 - 10.1109/FUZZY.2010.5584280

DO - 10.1109/FUZZY.2010.5584280

M3 - Conference contribution/Paper

SN - 978-1-4244-6919-2

SP - 2652

EP - 2659

BT - IEEE International Conference on Fuzzy Systems (FUZZ), 2010

PB - IEEE

T2 - 2010 IEEE World Congress on Computational Intelligence

Y2 - 18 July 2010 through 23 July 2010

ER -